Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel

A modified SliceGAN architecture was proposed to generate a high-quality synthetic three-dimensional (3D) microstructure image of TYPE 316L material manufactured through additive methods. The quality of the resulting 3D image was evaluated using an auto-correlation function, and it was discovered th...

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Detalles Bibliográficos
Autores principales: Sugiura, Keiya, Ogawa, Toshio, Adachi, Yoshitaka, Sun, Fei, Suzuki, Asuka, Yamanaka, Akinori, Nakada, Nobuo, Ishimoto, Takuya, Nakano, Takayoshi, Koizumi, Yuichiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10218809/
https://www.ncbi.nlm.nih.gov/pubmed/37233310
http://dx.doi.org/10.3390/jimaging9050090
Descripción
Sumario:A modified SliceGAN architecture was proposed to generate a high-quality synthetic three-dimensional (3D) microstructure image of TYPE 316L material manufactured through additive methods. The quality of the resulting 3D image was evaluated using an auto-correlation function, and it was discovered that maintaining a high resolution while doubling the training image size was crucial in creating a more realistic synthetic 3D image. To meet this requirement, modified 3D image generator and critic architecture was developed within the SliceGAN framework.

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